Method, device, and system for processing image data representing a scene for extracting features

    公开(公告)号:US12125273B2

    公开(公告)日:2024-10-22

    申请号:US17510407

    申请日:2021-10-26

    Applicant: Axis AB

    CPC classification number: G06V10/95 G06V10/147 G06V10/25 G06V10/462

    Abstract: A method (100), a device (600;700) and a system (800) for processing image data representing a scene for extracting features related to objects in the scene using a convolutional neural network are disclosed. Two or more portions of the image data representing a respective one of two or more portions of the scene are processed (S110), by means of a respective one of two or more circuitries, through a first number of layers of the convolutional neural network to form two or more outputs, wherein the two or more portions of the scene are partially overlapping. The two or more outputs are combined (S120) to form a combined output, and the combined output is processed (S130) through a second number of layers of the convolutional neural network by means of one of the two or more circuitries for extracting features related to objects in the scene.

    Power management in processing circuitry which implements a neural network

    公开(公告)号:US11874721B2

    公开(公告)日:2024-01-16

    申请号:US17863094

    申请日:2022-07-12

    Applicant: Axis AB

    Inventor: Anton Jakobsson

    CPC classification number: G06F1/324 G06F1/206

    Abstract: A method of operating a hardware accelerator comprises: implementing a multi-layer neural network using the hardware accelerator; measuring a power consumption of the hardware accelerator while executing a predefined operation on the multi-layer network at a default clock frequency; evaluating one or more power management criteria for the measured power consumption; and, in response to exceeding one of the power management criteria, deciding to reduce the clock frequency relative to the default clock frequency. In the step of measuring a power consumption of the hardware accelerator, per-layer measurements which each relate to fewer than all layers of the neural network may be captured.

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